What does an AI model “understand” and why? A long-held belief is there are easy-to-interpret neurons -- or “class selective” neurons. For instance, finding neurons that

Contribute to MIC-DKFZ/nnUNet development by creating an account on GitHub.

Learn how to implement Linear Regressionin PyTorch, a very popular deep learning library that is being developed by Facebook. We are going to dive into the linear model. Our objective here is to use the data to predict the output using a linear model. Here…

Leveraging BERT and TF-IDF to create easily interpretable topics.

NeurIPS 2020 Institute of Automation CAS and Microsoft Research Asia paper presents an attention-based decoder that integrates CV object representations

The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.

Hivemind is a library to train large neural networks across the internet. Imagine training one huge transformer on thousands of computers from universities, companies, and volunteers.

Modular Primitives for High-Performance Differentiable Rendering

Overfitting is a common explanation for the poor performance of a predictive model. An analysis of learning dynamics can help to identify whether a model has overfit the training dataset and may suggest an alternate configuration to use that could result…

Google Brain ICLR 2021 submission analyzes learned optimizers’ performance advantage over well-tuned baseline optimizers.

Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer

As more engineers and developers explore Software 2.0, we’ve extended the Kotlin compiler to support differentiability and tensor typing.

With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.